


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'layoutlmv3'
    def __init__(			self     ,   __snake_case=5_0_2_6_5     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=5_1_2     ,   __snake_case=2     ,   __snake_case=0.02     ,   __snake_case=1E-5     ,   __snake_case=1     ,   __snake_case=0     ,   __snake_case=2     ,   __snake_case=1_0_2_4     ,   __snake_case=1_2_8     ,   __snake_case=1_2_8     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=1_2_8     ,   __snake_case=6_4     ,   __snake_case=2_5_6     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=2_2_4     ,   __snake_case=3     ,   __snake_case=1_6     ,   __snake_case=None     ,   **__snake_case     ,   ):
         super().__init__(
             vocab_size=__snake_case     ,   hidden_size=__snake_case     ,   num_hidden_layers=__snake_case     ,   num_attention_heads=__snake_case     ,   intermediate_size=__snake_case     ,   hidden_act=__snake_case     ,   hidden_dropout_prob=__snake_case     ,   attention_probs_dropout_prob=__snake_case     ,   max_position_embeddings=__snake_case     ,   type_vocab_size=__snake_case     ,   initializer_range=__snake_case     ,   layer_norm_eps=__snake_case     ,   pad_token_id=__snake_case     ,   bos_token_id=__snake_case     ,   eos_token_id=__snake_case     ,   **__snake_case     ,   )
         snake_case											=							max_ad_position_embeddings
         snake_case											=							coordinate_size
         snake_case											=							shape_size
         snake_case											=							has_relative_attention_bias
         snake_case											=							rel_pos_bins
         snake_case											=							max_rel_pos
         snake_case											=							has_spatial_attention_bias
         snake_case											=							rel_ad_pos_bins
         snake_case											=							max_rel_ad_pos
         snake_case											=							text_embed
         snake_case											=							visual_embed
         snake_case											=							input_size
         snake_case											=							num_channels
         snake_case											=							patch_size
         snake_case											=							classifier_dropout






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.12'      )
    @property
    def 						a_     (			self       ):
         # The order of inputs is different for question answering and sequence classification
         if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]       )
         else:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                  ]       )
    @property
    def 						a_     (			self       ):
         return 1E-5
    @property
    def 						a_     (			self       ):
         return 1_2




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = -1     ,   __snake_case = -1     ,   __snake_case = False     ,   __snake_case = None     ,   __snake_case = 3     ,   __snake_case = 4_0     ,   __snake_case = 4_0     ,   ):
         setattr(processor.image_processor     ,   '''apply_ocr'''     ,   __snake_case       )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         snake_case											=							compute_effective_axis_dimension(
             __snake_case     ,   fixed_dimension=OnnxConfig.default_fixed_batch     ,   num_token_to_add=0       )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         snake_case											=							processor.tokenizer.num_special_tokens_to_add(__snake_case       )
         snake_case											=							compute_effective_axis_dimension(
             __snake_case     ,   fixed_dimension=OnnxConfig.default_fixed_sequence     ,   num_token_to_add=__snake_case       )
         # Generate dummy inputs according to compute batch and sequence
         snake_case											=							[[''' '''.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         snake_case											=							[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         snake_case											=							self._generate_dummy_images(__snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       )

         snake_case											=							dict(
             processor(
                 __snake_case     ,   text=__snake_case     ,   boxes=__snake_case     ,   return_tensors=__snake_case     ,   )       )

         return inputs








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




